BOSVDD-Based GNSS Spoofing Detection for Rail Vehicle Positioning

The vulnerability of Global Navigation Satellite System (GNSS) may result in great risks and insufficiencies under the threaten of a GNSS spoofing attack, which constrains its utilization in safety-related applications, such as rail signaling and collision avoidance. An important strategy for safely utilizing GNSS in the rail vehicle system is the detection and identification of the spoofing attack status. In this work, we present an advanced spoofing detection solution for GNSS-based rail vehicle positioning, which consists of the signal parameter estimation phase and the spoofing detection phase. To determine the composition of the satellite signal, an estimation method is proposed to decompose the correlation function of the signal by combining the least absolute shrinkage and selection operator (LASSO) and least squares (LS). The optimal signal parameters of the two subsignals are determined, including signal amplitude and code delay. Three spoofing detection indicators are designed according to the relative change relationship of estimated parameters. For a difficult-to-detect synchronous-induced spoofing attack, a model-based unsupervised GNSS spoofing detection solution is proposed using a novel Bayesian optimized support vector data description (BOSVDD) method. The model integrates the characteristics of the three detection indicators to determine whether the system is attacked by spoofing. We apply our proposed solution under specific GNSS spoofing scenarios using the experimental results from a GNSS spoofing-injection test platform. Furthermore, we show the performance benefit of our solution compared to state-of-the-art parameter estimation strategies and spoofing detection methods.